If your company is trying to hire a data scientist, proceed with caution. Given the shortage of data science talent, more candidates are assuming the title hoping to command a higher salary. Actual data scientists are much harder to find, and they're harder to keep because they're in high demand.
"The way I define a data scientist is somebody who knows programming better than a statistician and more statistics than a programmer. Both of those traits are table stakes," said Anthony Goldbloom, cofounder and CEO of data science competition platform Kaggle, in an interview.
Business domain knowledge is also important, since data scientists need to understand the problem they're solving and its context. Increasingly, organizations recruiting data scientists are also looking for machine learning experience, since the capability is necessary to keep pace with data growth, particularly with the addition of IoT devices. Data scientists should also be, but aren't always, effective communicators.
"You have to understand how to talk to people in a way that's simple and comprehensible to them while maintaining accuracy," said Alexander Isakov, CEO of business data solutions and strategy firm Pallantius. "CEOs and senior management don't care if we use a random forest or Oracle Delphi. As long as we clearly explain what's going on and how to make it actionable."
Everyone wants to hire unicorns -- those rare beings who are equally good at math, statistics, computer science, domain knowledge, communication, and perhaps machine learning. Since hiring a unicorn is difficult at best, organizations need to make compromises. They need to be mindful of the compromises they're making and why.
"You need to start by answering this simple question: 'What problem are you trying to solve?' Once you know what your business goal is, you can start both looking for the talent you need and the right tools," said Judith Hurwitz, president and CEO of consulting firm Hurwitz & Associates.
If you're considering hiring a data scientist, why not consider building a data science capability? It may well be a wiser long-term strategy.
"If you're adding talent, then you want to be very conscious of building a rounded team," said Wilds Ross, principal of data and analytics at audit, tax, and advisory service firm KPMG.
"You want to have some statistics, data engineering, optimization, [and] computer science. You're going to want to define what your objectives are in deploying a data science team in your organization and decide what we're really going to work on in the business to improve."
The best data scientists have a few traits and qualifications worth noting. We've identified some of them in the next pages. What would you add to our list?